Gadolinium-based contrast agents (GBCAs) have become a cornerstone in clinical routine for detection, characterization and monitoring of several diseases. Particularly, GBCAs are clinically relevant for the detection of blood brain barrier (BBB) damage, which is associated with an aggressive tumor behavior. However, issues such as safety concerns related to deposition of GBCA in the brain, prolonged acquisitions, and cost increase advocate against its usage. In this work, we propose a novel approach based on a cascade of deep networks for pre- and post-contrast parametric mapping and the synthesis of post-contrast T1-weighted images. Only a pair of pre-contrast weighted images acquired with conventional pulse sequences are used as inputs; thus, our approach is GBCAs-free. Results reveal the potential of this approach to obtain T1w-enhancement information after tumor resection which is comparable with another state-of-the-art prediction approach. We provide not only the predictions, but also the pre- and post-contrast parametric maps without the usage of GBCAs.@en

Background: Gadolinium-based contrast agents (GBCAs) are usually employed for glioma diagnosis. However, GBCAs raise safety concerns, lead to patient discomfort and increase costs. Parametric maps offer a potential solution by enabling quantification of subtle tissue changes without GBCAs, but they are not commonly used in clinical practice due to the need for specifically targeted sequences. This work proposes to predict post-contrast T1-weighted enhancement without GBCAs from pre-contrast conventional weighted images through synthetic parametric maps computed with generative artificial intelligence (deep learning). Methods: In this retrospective study, three datasets have been employed: (I) a proprietary dataset with 15 glioma patients (hereafter, GLIOMA dataset); (II) relaxometry maps from 5 healthy volunteers; and (III) UPenn-GBM, a public dataset with 493 glioblastoma patients. A deep learning method for synthesizing parametric maps from only two conventional weighted images is proposed. Particularly, we synthesize longitudinal relaxation time (T1), transversal relaxation time (T2), and proton density (PD) maps. The deep learning method is trained in a supervised manner with the GLIOMA dataset, which comprises weighted images and parametric maps obtained with magnetic resonance image compilation (MAGiC). Thus, MAGiC maps were used as references for the training. For testing, a leave-one-out scheme is followed. Finally, the synthesized maps are employed to predict T1-weighted enhancement without GBCAs. Our results are compared with those obtained by MAGiC; specifically, both the maps obtained with MAGiC and the synthesized maps are used to distinguish between healthy and abnormal tissue (ABN) and, particularly, tissues with and without T1-weighted enhancement. The generalization capability of the method was also tested on two additional datasets (healthy volunteers and the UPenn-GBM). Results: Parametric maps synthesized with deep learning obtained similar performance compared to MAGiC for discriminating normal from ABN (sensitivities: 88.37% vs. 89.35%) and tissue with and without T1-weighted enhancement (sensitivities: 93.26% vs. 87.29%) on the GLIOMA dataset. These values were comparable to those obtained on UPenn-GBM (sensitivities of 91.23% and 81.04% for each classification). Conclusions: Our results suggest the feasibility to predict T1-weighted-enhanced tissues from pre-contrast conventional weighted images using deep learning for the synthesis of parametric maps.

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